[PDF] Top 20 Co Occurrence Cluster Features for Lexical Substitutions in Context
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Co Occurrence Cluster Features for Lexical Substitutions in Context
... sentence-based co-occurrence statistics from a large corpus are used as a basis to to construct several word graphs for different ...Significant co-occurrences between all content words (nouns, ... See full document
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Investigation into Human Preference between Common and Unambiguous Lexical Substitutions
... A number of modifications were made to the at- tributes in order to make them more suitable for the machine-learning process. It was found that the con- text score had an extremely long tail, and taking the logs of each ... See full document
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Learning to Rank Lexical Substitutions
... non-lexical features from heterogeneous evidence such as lexical-semantic resources and distributional similarity, n-gram counts and shallow syntactic fea- tures computed on large, unannotated ... See full document
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Building Semantic Perceptron Net for Topic Spotting
... word occurrence based cluster abstraction model that learns a hierarchical topic ...the context or lexical chain relations among the ... See full document
8
Learning Syntactic Categories Using Paradigmatic Representations of Word Context
... using context information alone without the identity or the features of the target word ...the co-occurrence of word and context types is essential for inducing syntactic ...word ... See full document
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Supervised Ranking of Co occurrence Profiles for Acquisition of Continuous Lexical Attributes
... of lexical resources for sentiment anal- ysis is a commonly-addressed ...of co-occurrence based on dimensionality ...(6-word) context around the ...statistical features, including ... See full document
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Modeling geometric temporal context with directional pyramid co occurrence for action recognition
... Pyramid Co-occurrence Matrix (DPCM) to describe the spatio-temporal distribution of the vector-quantized local feature descriptors extracted from a ...the co-occurrence statistics of local ... See full document
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Combining lexical and context features for automatic ontology extension
... based-methods can also be used to automatically or semi- automatically construct and extend ontologies [9, 10]. For example, Lee et al. [11] focus on text mining of relations that are asserted in text between mentions of ... See full document
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Co occurrence Retrieval: A Flexible Framework for Lexical Distributional Similarity
... different co-occurrence retrieval models ...different co-occurrence ...weight features gave improved performance on the two evaluation tasks over type- based or token-based ... See full document
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A Metric based Framework for Automatic Taxonomy Induction
... and cluster words based on similarities of the vectors (Brown et ...contextual features, the vec- tors can also be represented by verb-noun rela- tions (Pereira et ...2005), co-occurrence ... See full document
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Combining Multiple Knowledge Sources for Dialogue Segmentation in Multimedia Archives
... ing question is whether it is possible to automat- ically select the discriminative features for recog- nition. This is particularly important for prosodic features, because the direct modelling approach we ... See full document
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Intelligent Arabic Sign Language to Arabic text Translation for Easy Deaf Communication
... extracted features used are combining intensity histogram features and Gray Level Co-occurrence Matrix (GLCM) features, Experiments revealed that the proposed system was able to ... See full document
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Automatic Word Sense Discrimination
... Similarity in Word Space is based on second-order co-occurrence: two tokens or contexts of the ambiguous word are assigned to the same sense cluster if the words they co-occur with in tu[r] ... See full document
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Investigating the Opacity of Verb Noun Multiword Expression Usages in Context
... the context of the occurrences of expressions in or- der to determine whether they are ...of context features extracted from this special-purpose lan- guage resource for identification of ... See full document
6
What Decisions Have You Made?: Automatic Decision Detection in Meeting Conversations
... of features used in this study: unigrams (LX1), prosodic (PROS), contextual (CONT), and topical (TOPIC) ...prosodic features can be gen- erated fully ...of features we used for training models can be ... See full document
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IJCSMC, Vol. 2, Issue. 9, September 2013, pg.172 – 177 RESEARCH ARTICLE A New Color-Texture Approach for Products Inspection using Wavelets
... When compare with the other methods in various texture classification systems like Calculation of GLCM of an Image and calculate Texture features such as Energy, Contrast, Entropy, Homogeneity and Correlation. The ... See full document
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Carrier Sentence Selection for Fill in the blank Items
... readable, features used in text readability predic- tion are not directly transferrable to our ...these features are intended for documents, and may not work well when applied on single sen- ...adapts ... See full document
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Automatic Extraction of Entity Alias from the Web
... For forming a graph, we consider co occur between name and alias and also consider number of times alias appears with real name on that link. Graph-based representations of real- world problems have been helpful ... See full document
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SEMHE: A Generalised Two level System
... The arguments are: 1 a rule identifier, id; 2 the left-lexical-context, LLC, the lexical center, Lex, and the right-lexical-context, RLC, each in the form of a list-of-lists, where the i[r] ... See full document
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A Method for Correcting Errors in Speech Recognition Using the Statistical Features of Character Co occurrence
... A Method for Correcting Errors in Speech Recognition Using the Statistical Features of Character Co occurrence A Method for Correcting Errors in Speech Recognition Using the Statistical Features of Ch[.] ... See full document
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